Expressing Trust with Temporal Frequency of User Interaction in Online Communities

Reputation systems concern soft security dynamics in diverse areas. Trust dynamics in a reputation system should be stable and adaptable at the same time to serve the purpose. Many reputation mechanisms have been proposed and tested over time. However, the main drawback of reputation management is that users need to share private information to gain trust in a system such as phone numbers, reviews, and ratings. Recently, a novel model that tries to overcome this issue was presented: the Dynamic Interaction-based Reputation Model (DIBRM). This approach to trust considers only implicit information automatically deduced from the interactions of users within an online community. In this primary research study, the Reddit and MathOverflow online social communities have been selected for testing DIBRM. Results show how this novel approach to trust can mimic behaviors of the selected reputation systems, namely Reddit and MathOverflow, only with temporal information

Expressing Trust with Temporal Frequency of User Interaction in Online Communities

Reputation systems concern soft security dynamics in diverse areas. Trust dynamics in a reputation system should be stable and adaptable at the same time to serve the purpose. Many reputation mechanisms have been proposed and tested over time. However, the main drawback of reputation management is that users need to share private information to gain trust in a system such as phone numbers, reviews, and ratings. Recently, a novel model that tries to overcome this issue was presented: the Dynamic Interaction-based Reputation Model (DIBRM). This approach to trust considers only implicit information automatically deduced from the interactions of users within an online community. In this primary research study, the Red-dit and MathOverflow online social communities have been selected for testing DIBRM. Results show how this novel approach to trust can mimic behaviors of the selected reputation systems, namely Reddit and MathOverflow, only with temporal information

Quality of Information in Mobile Crowdsensing: Survey and Research Challenges

Smartphones have become the most pervasive devices in people's lives, and are clearly transforming the way we live and perceive technology. Today's smartphones benefit from almost ubiquitous Internet connectivity and come equipped with a plethora of inexpensive yet powerful embedded sensors, such as accelerometer, gyroscope, microphone, and camera. This unique combination has enabled revolutionary applications based on the mobile crowdsensing paradigm, such as real-time road traffic monitoring, air and noise pollution, crime control, and wildlife monitoring, just to name a few. Differently from prior sensing paradigms, humans are now the primary actors of the sensing process, since they become fundamental in retrieving reliable and up-to-date information about the event being monitored. As humans may behave unreliably or maliciously, assessing and guaranteeing Quality of Information (QoI) becomes more important than ever. In this paper, we provide a new framework for defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the current state-of-the-art on the topic. We also outline novel research challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN

Which Positive Feedback Matters? The Role of Language Concreteness and Temporal Effect in Continuous Contribution in Open Innovation Community

The feedback mechanism is the basis for motivating users to make continuous contributions in the Open Innovation Community (OIC). Although previous studies have revealed the overall role of positive feedback in promoting continuous user contribution, it is not clear which type of positive feedback is more effective and how it changes over time. To solve these problems, we constructed a research model based on reinforcement theory and took Lego Ideas, a typical OIC, as the research object to crawl users’ ideas and feedback data for empirical analysis. The results confirmed the effect of positive feedback and further demonstrated that, the effectiveness of positive feedback varies based on feedback concreteness and the tenure of the focal user. Our study contributes to the literature on how feedback affects user contributions in online communities by refining the classifications of feedback, and provide practical guidance for companies to motivate users to contributing ideas continuously

A Computational Model of Trust Based on Dynamic Interaction in the Stack Overflow Community

A member’s reputation in an online community is a quantified representation of their trustworthiness within the community. Reputation is calculated using rules-based algorithms which are primarily tied to the upvotes or downvotes a member receives on posts. The main drawback of this form of reputation calculation is the inability to consider dynamic factors such as a member’s activity (or inactivity) within the community. The research involves the construction of dynamic mathematical models to calculate reputation and then determine to what extent these results compare with rules-based models. This research begins with exploratory research of the existing corpus of knowledge. Constructive research in the building of mathematical dynamic models and then empirical research to determine the effectiveness of the models. Data collected from the Stack Overflow (SO) database is used by models to calculate a rule-based and dynamic member reputation and then using statistical correlation testing methods (i.e., Pearson and Spearman) to determine the extent of the relationship. Statistically significant results with moderate relationship size were found from correlation testing between rules-based and dynamic temporal models. The significance of the research and its conclusion that dynamic and temporal models can indeed produce results comparative to that of subjective vote-based systems is important in the context of building trust in online communities. Developing models to determine reputation in online communities based upon member post and comment activity avoids the potential drawbacks associated with vote-based reputation systems

Learning with comments: An analysis of comments and community on Stack Overflow

Stack Overflow (SO) has become a primary source for learning how to code, with community features supporting asking and answering questions, upvoting to signify approval of content, and comments to extend questions and answers. While past research has considered the value of posts, often based on upvoting, little has examined the role of comments. Beyond value in explaining code, comments may offer new ways of looking at problems, clarifications of questions or answers, and socially supportive community interactions. To understand the role of comments, a content analysis was conducted to evaluate the key purposes of comments. A coding schema of nine comment categories was developed from open coding on a set of 40 posts and used to classify comments in a larger dataset of 2323 comments from 50 threads over a 6-month period. Results provide insight into the way the comments support learning, knowledge development, and the SO community, and the use and usefulness of the comment feature